{"rid":"storage/openxlab-model-dataset-centers","card":{"rid":"storage/openxlab-model-dataset-centers","eid":"openxlab","topic":"storage","slug":"openxlab-model-dataset-centers","provider":"OpenXLab 浦源 (Shanghai AI Lab) — model + dataset centers","url":"https://openxlab.org.cn","domain":"openxlab.org.cn","category":"model-hub","tier":null,"verdict":"qualifies-with-caveat","vorder":1,"grp":"usable","confidence":"inferred","binding":"none","identity_kind":"email","identity_basis":"stated","needs_card":0,"needs_phone":1,"needs_id":0,"needs_institution":0,"country":null,"crypto":null,"interfaces":"rest,git,cli","egress_metered":0,"per_object_bytes":null,"retention_days":null,"verified_at":"2026-10-21T00:00:00.000Z","verified_basis":"inherited","checked_on":"2026-10-21","stale_on":"2026-11-20","n_evidence":3,"usd_month":null,"usd_per_tb_month":null,"summary":"Not published, and this is the honest finding. The platform's own intro describes the model center as building an object-storage layer specifically for large...","duplicate_of":null,"n_unknown":6},"record":{"topic":"storage","slug":"openxlab-model-dataset-centers","provider":"OpenXLab 浦源 (Shanghai AI Lab) — model + dataset centers","url":"https://openxlab.org.cn","category":"model-hub","free_tier":{"stated":"Not published, and this is the honest finding. The platform's own intro describes the model center as building an object-storage layer specifically for large files — verbatim: '基于平台内的命令行工具，便于 AI 开发者上传和发布模型文件，搭建对象存储体系，提供大文件存储能力，快速上传下载功能，便于 AI 开发者进行模型存储' (object storage system providing large-file storage capability). The dataset center supports private datasets as well as public ones. But no GB number appears on any page I could reach; the openxlab.org.cn docs tree 504'd repeatedly and the upload pages contain no quota statement.","unit":"unlimited","metered_on":"stored-bytes","source_url":"https://openxlab.org.cn/docs/intro.html","read_on":"2026-10-21","notes":null},"per_object_cap":{"stated":"not stated","source_url":"https://openxlab.org.cn/docs/intro.html","notes":"not stated"},"retention":{"stated":"not stated","source_url":"https://openxlab.org.cn/docs/intro.html","notes":"not stated"},"egress":{"terms":"No egress meter found; public models and datasets are anonymously downloadable. Reads are CDN-served. Unverified at the header level because model-detail pages timed out from this network.","metered":false},"interface":["rest","git","cli"],"interface_detail":"rest","tooling":"pip install openxlab, then first-party subcommands: `openxlab dataset upload-file`, `openxlab dataset upload-folder`, `openxlab model upload ...` with --dataset-repo/--source-path/--target-path. Python SDK mirrors the CLI (upload_file / upload_folder). Also git+LFS. Real, named, documented client — this passes the tooling filter cleanly.","chaining":"Repos are per-user and per-org and free to create, so capacity is N repos. OpenXLab is the Shanghai AI Lab's public front door (InternLM, OpenDataLab datasets), so its per-repo sizes are already in the tens-of-GB range. Multiplying across accounts is easy but the per-account ceiling is unknown.","limits":[],"limits_note":"Unverified. What I can say: uploads go through a chunked SDK rather than a web form; dataset upload supports 文件/文件夹/压缩包 with server-side auto-extract for archives (a 50 GB zip becomes an extracted tree — that is a quota-relevant behaviour, since the extracted size can exceed the archive). No published per-file cap, retention period, or rate limit found.","identity_cost":{"kind":"email","detail":"email, plus likely +86 phone — Chinese platform, and prior rounds recorded the pans in this family demanding real-name verification.","country":null},"reliability_note":"Model pages return 302 to login for detail views.","confidence":"inferred","confidence_note":"One page read moves it to documented.","evidence":[{"url":"https://openxlab.org.cn/docs/intro.html","date":"2026-10-21","proved":"read live: model center provides '对象存储体系，提供大文件存储能力' and the dataset center supports '私有数据集的上传、管理和发布'"},{"url":"https://opendatalab.com/docs/datasets/上传数据集.html","date":"2026-10-21","proved":"read live: three upload modes (file / folder / archive with auto-extract), plus CLI and Python SDK (`pip install openxlab`, `openxlab dataset upload-folder`)"},{"url":null,"date":"2026-10-21","proved":"unverified: no quota page exists that I could reach; openxlab.org.cn docs paths 504'd repeatedly and opendatalab.com API paths timed out at 25-30 s"}],"verdict":"qualifies-with-caveat","verified_at":"2026-10-21T00:00:00.000Z","notes":"Not published, and this is the honest finding. The platform's own intro describes the model center as building an object-storage layer specifically for large files — verbatim: '基于平台内的命令行工具，便于 AI 开发者上传和发布模型文件，搭建对象存储体系，提供大文件存储能力，快速上传下载功能，便于 AI 开发者进行模型存储' (object storage system providing large-file storage capability). The dataset center supports private datasets as well as public ones. But no GB number appears on any page I could reach; the openxlab.org.cn docs tree 504'd repeatedly and the upload pages contain no quota statement.","extras":{},"binding":"none","binding_note":"no constraint in the record binds at this scale; the record carries no limit of a kind the vocabulary names"},"claims":[{"path":"$.free_tier.stated","kind":"free-quota","num":null,"unit":null,"per":null,"currency":null,"usd":null,"fx_date":null,"parse":"unparsed"}],"unknown":[["$.evidence[2].url","private-url"],["$.free_tier.notes","process"],["$.per_object_cap.notes","not-extracted"],["$.per_object_cap.stated","not-extracted"],["$.retention.notes","not-extracted"],["$.retention.stated","not-extracted"]],"history":[],"drift":[]}